To develop a breast cancer risk model to identify women at mammographic screening who are at higher risk of breast cancer within the general screening population. This retrospective nested case-control study used data from a population-based breast screening program (2009–2015). All women aged 40–75 diagnosed with screen-detected or interval breast cancer (n = 1882) were frequency-matched 3:1 on age and screen-year with women without screen-detected breast cancer (n = 5888). Image-derived risk factors from the screening mammogram (percent mammographic density [PMD], breast volume, age) were combined with core biopsy history, first-degree family history, and other clinical risk factors in risk models. Model performance was assessed using the area under the receiver operating characteristic curve (AUC). Classifiers assigning women to low- versus high-risk deciles were derived from risk models. Agreement between classifiers was assessed using a weighted kappa. The AUC was 0.597 for a risk model including only image-derived risk factors. The successive addition of core biopsy and family history significantly improved performance (AUC = 0.660, p < 0.001 and AUC = 0.664, p = 0.04, respectively). Adding the three remaining risk factors did not further improve performance (AUC = 0.665, p = 0.45). There was almost perfect agreement (kappa = 0.97) between risk assessments based on a classifier derived from image-derived risk factors, core biopsy, and family history compared with those derived from a model including all available risk factors. Women in the general screening population can be risk-stratified at time of screen using a simple model based on age, PMD, breast volume, and biopsy and family history. • A breast cancer risk model based on three image-derived risk factors as well as core biopsy and first-degree family history can provide current risk estimates at time of screen. • Risk estimates generated from a combination of image-derived risk factors, core biopsy history, and first-degree family history may be more valid than risk estimates that rely on extensive self-reported risk factors. • A simple breast cancer risk model can avoid extensive clinical risk factor data collection.

Background: Women attending mammography screening units (msus) and well women’s clinics (wwcs) represent a motivated cohort likely to engage in interventions aimed at primary breast cancer (bca) prevention. Methods: We used a feasibility questionnaire distributed to women (40–49 or 50–74 years of age) attending msus and wwcs in Halifax, Nova Scotia, to examine (1) women’s views about bca primary prevention and sources of health care information, (2) prevalence of lifestyle-related bca risk factors, and, (3) predictors of prior mammography encounters within provincial screening guidelines. Variables examined included personal profiling, comorbidities, prior mammography uptake, lifestyle behaviours, socioeconomic status, health information sources, and willingness to discuss or implement lifestyle modifications, or endocrine therapy, or both. A logistic regression analysis examined associations with prior mammography encounters. Results: Of the 244 responses obtained during 1.5 months from women aged 40–49 years (n = 75) and 50–74 years (n = 169), 56% and 75% respectively sought or would prefer to receive health information from within, as opposed to outside, health care. Lifestyle-related bca risk factors were prevalent, and most women were willing to discuss or implement lifestyle modifications (93%) or endocrine therapy (67%). Of the two age groups, 49% and 93% respectively had previously undergone mammography within guidelines. Increasing age and marital status (single, separated, or divorced vs. married or partnered) were independent predictors of prior mammography encounters within guidelines for women 40–49 years of age; no independent predictors were observed in the older age group. Conclusions: Women attending msus and wwcs seem to largely adhere to mammography guidelines and appear motivated to engage in bca primary prevention strategies, including lifestyle modifications and endocrine therapy. Women’s views as observed in this study provide a rationale for the potential incorporation of bca risk assessment within the “mammogram point of care” to engage motivated women in bca primary prevention strategies.

e12556 Background: Little is known about the association between mammographic breast density and the subtypes of breast cancer including HER2-positive breast cancers (HER2-BrCa). The objective of this study was to assess the strength of association between breast density and HER2-BrCa in a population-based screening program. Methods: This is a population-based case-control breast cancer study of women aged 40 to 75 who underwent digital breast screening from 2009 to 2015 in Nova Scotia, Canada. Cases included women diagnosed with HER2-BrCa at screen or before their next screen (interval); controls included women without screen-detected cancer matched to cases by age and year of screen. Measures of mammographic breast density (percent density, BI-RADS-4th and -5th edition) were obtained from automated software (densitasai) and linked with clinical risk factor data (age, parity, total breast volume, post-menopausal status, hormone replacement therapy, family history and history of core biopsy). The association between breast density and cancer risk was assessed by calculating the odds ratios [OR] with 95% confidence intervals using multivariable logistic regression. Results: A total of 209 cases (median age, 58.8 years) and 6812 controls (median age, 59.4 years) were included. The risk of HER2-BrCa increased with increasing levels of percent breast density. High breast density according to BIRADS-4th and -5th editions was significantly associated with HER2-BrCa: BIRADS -4th 3/4 vs 1: OR 2.50 (1.68 - 3.68); BIRADS-5th C/D vs A: OR 2.58 (1.71 - 4.01). The association between higher breast density and increased risk of HER2-BrCa remained after adjustment for clinical factors. Conclusions: The risk of HER2-BrCa was associated with progressively higher mammographic breast density, although to a lesser extent than breast cancer in general. Accurate risk models including breast density may support the development of more breast-screening protocols that can lead to more strategic use of healthcare resources.

OBJECTIVE Measures of percent mammographic density (PMD) are often categorized using various density scales. The purpose of this study was to examine information loss associated with the use of categorical density scales. METHODS Baseline PMD was assessed at 1% precision for 2,374 females. The data were used to create 21-category, 4-category and 2-category density scales. R-squared and root mean square error were used to evaluate the effect of categorizing PMD. The area under the receiver operator characteristic curves were compared between cancer risk models employing solely categorical PMD scales and solely baseline PMD for a subset of females (424 cases, 848 controls). RESULTS R-squared value decreased from 1.00 (1% PMD) to 0.56 (2-category scale), while root mean square error increased from 0.00 (1% PMD) to 10.83 (2-category scale). The area under the receiver operator characteristic curve decreased from 0.64 for a cancer risk model using 1% PMD to 0.58 for a risk model using a 21-category density scale (p < 0.0001), 0.55 for a 4-category Breast Imaging, Reporting and Data System-like scale (p < 0.0001) and 0.50 for a 2-category Breast Imaging, Reporting and Data System-like scale (high vs low) (p < 0.0001). CONCLUSION Categorizing PMD measures into categorical density scales leads to a significant loss of information. Indeed, a simple high versus low split of PMD using a 50% cut point yields a cancer risk model with no discriminatory power. Advances in knowledge: Use of categorical mammographic density scales rather than continuous percent mammographic density measures leads to significant loss of information. Breast cancer risk models using categorical mammographic density scales perform more poorly than models using continuous PMD measures.

Poster: ECR 2016 / C-2316 / The BI-RADS 5th edition density scale and breast cancer risk: a case-control study by: M. Abdolell 1, K. Tsuruda1, C. B. Lightfoot1, P. Brown1, S. A. Raza2, G. Schaller1, J. Caines1, J. I. Payne1, S. Iles1; 1Halifax, NS/CA, 2Sydney, NS/CA

Discussions of percent breast density (PD) and breast cancer risk implicitly assume that visual assessments of PD are comparable between vendors despite differences in technology and display algorithms. This study examines the extent to which visual assessments of PD differ between mammograms acquired from two vendors. Pairs of "for presentation" digital mammography images were obtained from two mammography units for 146 women who had a screening mammogram on one vendor unit followed by a diagnostic mammogram on a different vendor unit. Four radiologists independently visually assessed PD from single left mediolateral oblique view images from the two vendors. Analysis of variance, intra-class correlation coefficients (ICC), scatter plots, and Bland-Altman plots were used to evaluate PD assessments between vendors. The mean radiologist PD for each image was used as a consensus PD measure. Overall agreement of the PD assessments was excellent between the two vendors with an ICC of 0.95 (95% confidence interval: 0.93 to 0.97). Bland-Altman plots demonstrated narrow upper and lower limits of agreement between the vendors with only a small bias (2.3 percentage points). The results of this study support the assumption that visual assessment of PD is consistent across mammography vendors despite vendor-specific appearances of "for presentation" images.

Male breast disease comprises a wide spectrum of benign and malignant processes. We present the spectrum of diseases encountered at our institution over the past 7 years (2007-2013) and correlate their radiological and histopathological appearances. Gynaecomastia is the most frequently encountered disease due to its association with a variety of causes. Male breast malignancies, though rare, must be considered. The most frequently encountered pathological characteristic is invasive and the predominant histologic subtypes are infiltrating ductal carcinomas.

This article provides an overview of atypical femoral fractures with a highlight on their radiographic findings. Potent antiresorptive agents such as bisphosphonates or denosumab have been associated with the development of such fractures. However, at this time, a causal association has not been conclusively established. Atypical femoral fractures are insufficiency fractures, which frequently present with bone pain. Early identification of characteristic radiographic features and withdrawal of antiresorptive therapy may prevent the development of completed atypical femoral fractures.

Objective: Various clinical risk factors, including high breast density, have been shown to be associated with breast cancer. The utility of using relative and absolute area-based breast density-related measures was evaluated as an alternative to clinical risk factors in cancer risk assessment at the time of screening mammography.Methods: Contralateral mediolateral oblique digital mammography images from 392 females with unilateral breast cancer and 817 age-matched controls were analysed. Information on clinical risk factors was obtained from the provincial breast-imaging information system. Breast density-related measures were assessed using a fully automated breast density measurement software. Multivariable logistic regression was conducted, and area under the receiver-operating characteristic (AUROC) curve was used to evaluate the performance of three cancer risk models: the first using only clinical risk factors, the second using only density-related measures and the third using both clinical risk factors and density-related measures.Results: The risk factor-based model generated an AUROC of 0.535, while the model including only breast density-related measures generated a significantly higher AUROC of 0.622 (p<0.001). The third combined model generated an AUROC of 0.632 and performed significantly better than the risk factor model (p< 0.001) but not the density-related measures model (p=0.097).Conclusion: Density-related measures from screening mammograms at the time of screen may be superior predictors of cancer compared with clinical risk factors.Advances in knowledge: Breast cancer risk models based on density-related measures alone can outperform risk models based on clinical factors. Such models may support the development of personalized breast-screening protocols.

Two letters to the editor concerning an article in the last issue, "Myth: Mammography for Breast Cancer Screening – Are We Doing More Harm Than Good?" and a response from the author of the article.

Poster: ECR 2014 / C-0914 / Breast density from full-field digital mammograms and breast cancer risk: a case-control study by: M. Abdolell, K. Tsuruda, J. I. Payne, S. E. Iles, C. B. Lightfoot, J. Caines; Halifax, NS/CA

e12530 Background: Triple Negative Breast Cancer (TNBC) accounts for approximately 10% of invasive breast malignancies and is characterized by a relative lack of adjuvant systemic therapeutic options. Our aim was to describe a population-based cohort of primary TNBC patients including detection method, breast density at diagnosis, pathologic characteristics, and clinical outcomes, as a function of age at diagnosis. Methods: All cases of primary TNBC in Nova Scotia, Canada were identified through the Information System of the Nova Scotia Breast Cancer Screening Program (NSBSP). The study population included all female patients who underwent an open surgical biopsy following an imaging procedure, between January1 2005-13, and for whom pathological confirmation of TNBC was documented. A descriptive analysis of subjects’ clinical profile was performed, stratified by age group at diagnosis (≤49, 50-59, 60-69, and ≥70). Survival analysis techniques were used to model both disease-free (DFS) and overall survival...

Phosphorus (P) is a limiting nutrient in many environments but plants and microbes have evolved with mechanisms for acquiring soil P, including the excretion of phosphatase enzymes. Molecular analysis of bacterial phosphatase genes can provide insight into biological P transformations and the contribution to soil P availability and plant uptake. To assess these relationships, soil and plant samples were collected from 12 organically-managed soybean fields varying in pH, labile P concentration, and potential phosphatase activity (pH 6.5) across Prince Edward Island, Canada. Real-time PCR was used to quantify bacterial phosphatase genes (phoC and phoD) in bulk and rhizosphere soil. Primers targeting class A (phoC) of the bacterial non-specific acid phosphatases (NSAPs) were designed and confirmed as effectively targeting phoC genes through sequencing, and phylogenetic comparison with acid phosphatase genes from Genbank. Across all sites, we found that labile P in bulk soil was negatively correlated with phoC and phoD gene abundance and phosphatase activity. In addition, phosphatase activity was consistently higher in rhizosphere compared to bulk soil and was significantly correlated with phoC (bulk soil only) and phoD (rhizosphere soil only) gene abundance. A positive relationship was observed between phosphatase activity, nodule weight, and plant P uptake. Quantification of bacterial genes involved in organic P transformations has been limited, with this study providing the first attempt at quantifying phoC genes in field soils.

This article provides an overview of atypical femoral fractures with a highlight on their radiographic findings. Potent antiresorptive agents such as bisphosphonates or denosumab have been associated with the development of such fractures. However, at this time, a causal association has not been conclusively established. Atypical femoral fractures are insufficiency fractures, which frequently present with bone pain. Early identification of characteristic radiographic features and withdrawal of antiresorptive therapy may prevent the development of completed atypical femoral fractures.